The iris surrounds the dark, inner pupil region of an eye and extends concentrically to the white sclera of the eye.
A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, 2004 discloses that the iris of the eye is a near-ideal biometric.
For the purposes of recognition, typically an image of an iris region is acquired in a dedicated imaging system that uses infra-red (IR) illumination with the eye aligned with the acquisition camera to bring out the main features of the underlying iris pattern.
An iris pattern is a gray-scale/luminance pattern evident within an iris region that can be processed to yield an iris code. The iris pattern can be defined in terms of polar co-ordinates and these are typically converted into rectangular coordinates prior to analysis to extract the underlying iris code.
An iris code is a binary sequence obtained after analysis of the iris pattern. A typical iris code contains 2048 bits. Note that some bits are effectively redundant, or ‘fragile’, as they are nearly always set to a ‘1’ or a ‘0’ as disclosed in K. Hollingsworth, K. W. Bowyer, and P. J. Flynn, “All Iris Code Bits are Not Created Equal,” 2007 First IEEE Int. Conf. Biometrics Theory, Appl. Syst., 2007. Some of these fragile bits can be predicted in advance and as they offer less differentiation, they are often ignored when determining a match.
Nonetheless, systems supporting the acquisition of iris data from mobile persons are known, for example, as disclosed in J. R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. J. Lolacono, S. Mangru, M. Tinker, T. M. Zappia, and W. Y. Zhao, “Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments,” Proc. IEEE, vol. 94, 2006. This employs specialized lighting and requires people to walk along a specified path where multiple successive iris images are acquired under controlled lighting conditions. The system is proposed for airports where iris information is being used increasingly to verify passenger identity.
Separately, each of: C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. L. X. Li, “Multispectral Iris Analysis: A Preliminary Study,” 2006 Conf. Comput. Vis. Pattern Recognit. Work., 2006; M. Vilaseca, R. Mercadal, J. Pujol, M. Arjona, M. de Lasarte, R. Huertas, M. Melgosa, and F. H. Imai, “Characterization of the human iris spectral reflectance with a multispectral imaging system.,” Appl. Opt., vol. 47, pp. 5622-5630, 2008; and Y. Gong, D. Zhang, P. Shi, and J. Yan, “Optimal wavelength band clustering for multispectral iris recognition,” Applied Optics, vol. 51. p. 4275, 2012 suggest that iris patterns from lighter color eyes can be adequately acquired, but that eyes of darker color are difficult to analyze using visible light,
H. Proença and L. A. Alexandre, “Iris segmentation methodology for non-cooperative recognition,” IEE Proceedings—Vision, Image, and Signal Processing, vol. 153. p. 199, 2006; and A. E. Yahya and M. J. Nordin, “Non-cooperative iris recognition system: A review,” Inf. Technol. (ITSim), 2010 Int. Symp., vol. 1, 2010 disclose non-cooperative iris acquisition, typically obtained at a distance of 3-10 meters using directed IR sources. As imaging subsystems on smartphones continue to improve in quality of acquisition and as image analysis and post-processing technique also continue to improve, a point at which the quality of images from conventional digital cameras and smart-phones becomes of sufficient quality to analyze to a sufficient degree to determine some of the underlying features of an iris pattern will be reached.
For example U.S. Pat. No. 7,697,735 discloses identifying a person from face and iris data from a single 5 megapixel image. U.S. Pat. No. 7,697,735 provides recommended minimum sizes for face and eye features to enable a sufficiently accurate degree of recognition. However it does not specify any details of lighting or acquisition conditions and most iris acquisitions would not be of sufficient accuracy in an unconstrained use case. Nevertheless we note that the latest handheld devices can feature imaging subsystems with up to 40 megapixel resolutions and high power IR LEDs can be used to improve acquisition lighting conditions.
Other techniques such as high dynamic range (HDR) imaging combine more than one digital image to provide a combined image with improved image quality. This is a standard feature on most smartphone imaging systems and typically two images are acquired in sequence and combined, post-acquisition, to provide a sharper and higher quality final image. Techniques are well known in the literature to combine more than one image and as acquisition systems achieve higher frame rates (currently 60-120 frames per second for preview but likely to double with next-generation technology) it will be practical to capture as many as 8-10 images within the same time window used today to acquire two images. Taking advantage of sub-pixel registration or super-resolution techniques will therefore provide images with significantly higher local image contrast and sharpness than today's devices provide.
Thus it highly likely that images acquired with the next generation of imaging devices will be of sufficient quality to enable the determination of iris patterns from faces in standard images. This makes normal personal portraits and small-group photos a potential source for personal iris patterns with a high risk of such biometric information being used for a range of criminal activities ranging from identity theft, forging of personal identity documents up to gaining access to facilities protected by biometric security measures.
US 2009/0141946, Kondo discloses detecting an iris region of an eye from an original image and performing image conversion on the detected iris region so that feature data unique to the person cannot be extracted. For example, the iris region is divided into a plurality of portions and respective images of divided portions are re-arranged in a predetermined order or at random.
US 2010/0046805, Connell discloses generating a cancelable biometric including shifting at least one pixel region in a biometric image comprised of pixel regions. The pixel region is combined with at least one other pixel region to form a replacement region for the at least one pixel region to form a transformed image. The biometric image is reused to generate another transformed image if the transformed image is to be canceled.